Description
This dataset folder has been created for the replication of subjective well-being (SWB) fMRI study, 
aiming to investigate the neural substrate of wellbeing under two different perspectives: Inter- and Intra-personal. 
In the current version, 19 subjects preprocessed structural and functional MRI data with experimental protocols and behavior responses were uploaded. 
Also, analysis methods and results are separately provided in 'GLM.tar.gz', 'ParametricModulation.tar.gz', 'PsyPhysiInteraction.tar.gz',
'MultiVoxelPatternAnalysis.tar.gz', and 'EuclideanDistance.tar.gz'



Data structure
File: subj_VolumeTimeCourse.zip, subj_VolumeTimeCourse.z01, subj_VolumeTimeCourse.z02
19 subjects volume time course (VTC) data (117 runs in total, '*.vtc'), protocols ('*.prt'), and single-run design matrix files ('*.sdm')  

File: subj_anat.tar.gz
19 subjects preprocessed 3D anatomical data in .vmr format ('*.vmr').

File: GLM.tar.gz
For general linear model (GLM) analysis, multiple-subject design matrix data was provided (mdm_test2.mdm), 
the .mdm file can be modified to compute GLM with different protocol files (by replacing .prt with corresponding .vtc files lists) in text editor. 
The current version provides two different types of GLM models as results: one with categorical contrast (Interpersonal, Intrapersonal, Neutral; 'Hap_conds_VTC_N-117_RFX_PT_AR-2_ITHR-100.glm'), 
the other with happiness rating contrast (1 to 4 scale; 'value_VTC_N-117_RFX_PT_AR-2_ITHR-100.glm').

File: ParametricModulation.tar.gz
For parametric modulation, it is required to combine parametric regressors into protocol (.prt) files, 
To see the parametric modulatory effect of subjective happiness, the ‘*_para.prt’ files are made with behaviour response of subjective happiness rating. 
The results can be found in .glm files ('PM_VTC_N-117_RFX_PT_AR-2_ITHR-100.glm').

File: PsyPhysiInteraction.tar.gz
For psychophysiological interaction (PPI) analysis, the seed regions defined from Neurosynth (https://www.neurosynth.org/) 
in .voi format (dopamine_pFgA_z_FDR_0.01.vmp, dopamine.voi), multiple-subject design matrix (swb_category.mdm), 
and categorical GLM analysis results (swb_category.glm) were included. PPI was performed using Neuroelf(https://github.com/neuroelf/neuroelf-matlab), 
The results are saved in .vmp (PPI_dopamine_0.05_alphasim.vmp).
  
File: MultiVoxelPatternAnalysis.tar.gz
For Multi-voxel pattern analysis (MVPA) searchlight, trial estimated beta of 19 subjects was provided (‘*_PreOn-1-PostOn-8_z_t_Trials.vmp’). 
The princeton MVPA toolbox (https://github.com/PrincetonUniversity/princeton-mvpa-toolbox) was used in MATLAB to perform 
searchlight mapping (support vector machine (SVM) classifier with linear kernel function, more details in libsvm (https://www.csie.ntu.edu.tw/~cjlin/libsvm/), 
classifying between ‘Interpersonal’ and ‘Interpersonal’ conditions with leave one run out cross validation. 
Each subject’s estimated accuracy was saved in .vmp (2, 3, 5 size of searchlight radius; ‘*radius_ach_conn.vmp’). 
The ttest was used against the chance level (.5) to compute statistical results across all subject (results can be found in 'MVPA_linsvm_intra_inter_searchlight.vmp').   

File: EuclideanDistance.tar.gz
The trial estimated beta (‘*_PreOn-1-PostCond-6_z-t_Trials.vmp’) as an input, 
Euclidean distance from anterior commissure (AC) point to two ROIs (ACC/MPFC and PCC/Precuneus) was computed in MATLAB. 
(ACC/MPFC mask file: ‘mpfc_180518.voi’, PCC/Precuneus mask file: ‘precuneus_pcc_180518.voi’; both defined from meta-analysis based Neurosynth data).
The results can be found in .fig (MATLAB figure format)